Urban Water Planning in Lagos, Nigeria: An Analysis of Current Infrastructure Developments and Future Water Management Solutions

The state of Lagos, Nigeria is one of several African cities facing a major water crisis. Despite being rich in water resources, access to water within the state is dreadfully low and a major public health threat. Currently, the Lagos Water Corporation, the primary supplier of water in the state, is facing a 320-million-gallon water demand gap due to the rapidly growing population within the city. Additionally, the water crisis is further exacerbated by deteriorating infrastructure, political instability and poorly regulated water laws. The result of the water crisis in Lagos has led to detrimental consequences for its citizens as child mortality and water-borne disease related deaths have grown exponentially. To combat this problem, the Lagos Water Corporation developed its Water Supply Master Plan in an aim to bridge the water demand gap and provide pragmatic solutions for water-related issues within the state. Unfortunately, this plan is heavily flawed and non-encompassing. This paper will explore in detail the issues that aggravate the Lagos water crisis. Additionally, I will examine the Lagos Water Supply Plan and highlight its strengths and limitations, as well as provide probable solutions that can lead to a reform in the water crisis

Ein wiederentdeckter Hundefriedhof in Assiut

In der mittelägyptischen Stadt Assiut ist die Verehrung schakals- oder hundegestaltig vorgestellter Götter über mehr als 2000 Jahre nachweisbar. Neben der Verehrung von Upuaut und Anubis in Tempeln und den damit verbundenen heiligen Tieren sind auch Bestattungen von Caniden bekannt, deren Hintergrund ebenfalls die kultische Wertschätzung dieser Götter bilden dürfte. Der vorliegende Beitrag berichtet über die Arbeiten und Befunde an einem in Assiut erst vor wenigen Jahren entdeckten Hundefriedhof

Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers

Given an input video, its associated audio, and a brief caption, the audio-visual scene aware dialog (AVSD) task requires an agent to indulge in a question-answer dialog with a human about the audio-visual content. This task thus poses a challenging multi-modal representation learning and reasoning scenario, advancements into which could influence several human-machine interaction applications. To solve this task, we introduce a semantics-controlled multi-modal shuffled Transformer reasoning framework, consisting of a sequence of Transformer modules, each taking a modality as input and producing representations conditioned on the input question. Our proposed Transformer variant uses a shuffling scheme on their multi-head outputs, demonstrating better regularization. To encode fine-grained visual information, we present a novel dynamic scene graph representation learning pipeline that consists of an intra-frame reasoning layer producing spatio-semantic graph representations for every frame, and an inter-frame aggregation module capturing temporal cues. Our entire pipeline is trained end-to-end. We present experiments on the benchmark AVSD dataset, both on answer generation and selection tasks. Our results demonstrate state-of-the-art performances on all evaluation metrics.Comment: Accepted at AAAI 202

Scenario-Aware Audio-Visual TF-GridNet for Target Speech Extraction

Target speech extraction aims to extract, based on a given conditioning cue, a target speech signal that is corrupted by interfering sources, such as noise or competing speakers. Building upon the achievements of the state-of-the-art (SOTA) time-frequency speaker separation model TF-GridNet, we propose AV-GridNet, a visual-grounded variant that incorporates the face recording of a target speaker as a conditioning factor during the extraction process. Recognizing the inherent dissimilarities between speech and noise signals as interfering sources, we also propose SAV-GridNet, a scenario-aware model that identifies the type of interfering scenario first and then applies a dedicated expert model trained specifically for that scenario. Our proposed model achieves SOTA results on the second COG-MHEAR Audio-Visual Speech Enhancement Challenge, outperforming other models by a significant margin, objectively and in a listening test. We also perform an extensive analysis of the results under the two scenarios.Comment: Accepted by ASRU 202